Previous results [1]}, [2]} on training deep neural networks in practical tasks showed that, for each deep learning optimizer, the number of steps to train a deep neural network halves for each doubling of the batch size and there are diminishing returns beyond a critical batch size. Motivated by the results in [1]}, [2]}, we examine the relationship between batch size \(b\)  and the number of steps \(N\)  needed for training GAN, i.e., for finding a stationary local Nash equilibrium in the Nash equilibrium problem in GANs. Here, suppose that the generator runs in at most \(N_G\)  steps, defined for batch size \(b\)  by
\(N_G (b) := \frac{A_G b}{(\epsilon _G^2 - C_G)b -B_G},\) 
